At present, in banknote processing devices for the financial field such as cash circulator, banknote sorter, etc, a banknote recognition system has two main parts: a banknote classification learning system and a banknote recognition system, of which schematic structural diagrams are shown in FIG. 1 and FIG. 2. In the banknote classification learning system, banknote sample images to be learned are input, and a banknote classification model is output. In the banknote recognition system, a banknote sample image to be recognized is input, classification decision is performed on the sample through feature extraction and using the classification model acquired in the banknote classification learning system, and a final classification result is output.
For higher robustness of the banknote recognition system, i.e., to eliminate interference exerted by the quality of samples to be recognized on the recognition result as far as possible, abundant and diversified samples to be learned are normally input in the learning of the banknote classifier. When selecting samples, besides considering banknote samples in brand new condition, banknote samples in various conditions and banknote samples with contamination, incompletion, crack and folds in varying degrees need to be considered. Thus there is a large number of samples to be selected. Difficulty of the sample selection lies in the difficulty in collecting all types of banknotes in circulation, and particularly, for developing an algorithm with respect to foreign banknotes, it is almost impossible to collect a complete set of banknote samples. Generally speaking, the banknote samples to be learned mainly include the following types for selection: brand new condition, 80%-90% new condition, 70%-80% new condition, 0-70% new condition, contamination in varying degrees, incompletion in varying degrees, crack in varying degrees, and fold in a certain region, and if 30 banknotes are to be selected as samples for one type, 240 actually circulating banknotes satisfying the conditions are totally needed. If all needed types of banknotes may be completely collected to design a classifier, precision of the classifier may be ensured, and if the types of the samples are inadequate, it is possible that the precision of the classifier does not satisfy application requirement. However, to completely collect the various needed types of banknote samples in circulation, a large number of human resources and material resources may be needed, thereby affecting cost and efficiency for developing banknote recognition product; in other words, without extra cost spent to collect and screen the samples, the designed classifier may have decreased precision.
Hence, under the situation of limited number of selectable banknote samples, it is urgent for those skilled in the art to provide a method for recognizing and classifying banknotes and a system thereof to reduce extra cost while ensuring an improved classifier precision.